DE102022201922A1 - Method for planning and/or controlling and/or regulating a manufacturing process in a metallurgical production plant with several consecutive process steps - Google Patents

Abstract

The invention relates to a method for planning and/or controlling and/or regulating a production process in a metallurgical production plant with a number of successive process steps, comprising the steps of:Creating a base model (1) for mapping the material behavior in metal production and processing, the base model (1) uses the CALPHAD method (Calculation of Phase Diagrams) to describe the properties of these materials, creating at least one sub-model (2) which receives the information from the created base model (1) and processes it further, creating process models (3) for the multiple successive process steps of the manufacturing process in the metallurgical production plant, the process models created (3) optimizing the manufacturing process of the respective process step on the basis of one or more of the created sub-models (2), andoptimizing the manufacturing process in the metallurgical plant with the multiple successive process steps under Consideration of the created process models (3) and global and/or local optimization goals.

Description

The invention relates to a method for planning and/or controlling and/or regulating a production process in a metallurgical production plant with a number of successive process steps. The method according to the invention is based on a modeling of thermodynamic properties, thermophysical properties and/or properties of metallic alloys derived therefrom from the production process in the metallurgical plant.

The goal of every manufacturing process is to set certain properties on the product. In order to achieve these properties, certain process parameters must be set in the process, which also depend on the previous processes. This also applies to the microstructure of a primary material, which influences the process parameters of the subsequent process (e.g. the coiling temperature in the hot rolling mill affects the microstructure of the hot strip, which in turn influences the austenitization in a subsequent annealing line).

In EP 1 289 691 B2 describes a very simple state of the art of modeling phase transformations with empirical Avrami equations and solubility products. with the inside EP 1 289 691 B2 With the solubility product approach mentioned, only simple stoichiometric precipitations (contain only 2 components/alloying elements, such as aluminum nitride AIN) can be calculated. Complex precipitates cannot be calculated with this approach: e.g. mixed carbonitrides of vanadium with solubility for niobium and titanium (V, Nb, Ti)(C,N), which, depending on the C and N content, change from a carbide to a nitride or go in reverse. Cementite can not only consist of Fe3C, but also contain a high proportion of chromium (Fe,Cr,Mn,...)3(C). A precipitation of sigma phase in stainless steels is also not possible. The phase transition models are empirical, and their coefficients (Avrami model) must always be fitted to experimental data. Like regression models, they have no predictive power. Solubility products of excretions such as in S. Hahn, T. Schaden: "Dynaphase: Online Calculation of Thermodynamic Properties during Continuous Casting", Berg- und Hüttenmännische Zeitenhefte, BHM (2014) Vol. 159 (11), pp. 438-446 or R. Hinterkörner, N. Hübner, H. Resch, K. Burgstaller, W. Felberbauer, B. Tragl, "Slab-Quality Prediction with In-Depth Metallurgical Modeling", la metallurgia italiana, 55, 9/2004 disclosed only a very limited scope.

Solutions in which the thermodynamic model is not directly linked to the process model and where the calculated thermodynamic data are stored in tables or by means of regression coefficients do not allow, or only to a limited extent, the chemical composition currently being produced to be taken into account. It is also not possible to calculate the exact transition temperatures, phase fractions and compositions. This is particularly disadvantageous in the case of micro-alloy elements, in which small changes in the element contents (especially in the micro-alloy elements Nb, Ti, V) lead to large changes in the material behavior.

For products of austenite decomposition on cooling (e.g. proeutectoid ferrite formation) a very simple Avrami schema is used that is valid for isothermal transformations. The approach fails when the strand surface is reheated (core heat during intensive cooling), which leads to a redissolution of the ferrite formed.

These models are not universal and can only be used for a specific application. Use the solubility product to calculate AIN excretion EP 1 289 691 B2 cannot be used to predict, for example, the onset of ferrite formation, although nucleation first and then nucleation growth occurs in both processes.

In A. Samoilov, B. Buchmayr, H. Cerjak, "A thermodynamic model for composition and chemical driving force for nucleation of complex carbonitrides in microalloyed steel", Steel Research, 65, 1994, no. 7, p. 298 the Hillert-Staffanson model (precursor to the Compound Energy Formalism - CEF, limited to 2 sublattices) is applied to calculate carbonitrides of Nb, Ti, V. However, this model does not allow the simultaneous precipitation of other carbide types such as M23C6 with several sublattices (Cr,Fe,...)20(Cr,Fe,Mo,W,...)3(C,B)6 (see HL Lukas, SG Fries, B. Sundman: "Computational Thermodynamics", ISBN 978-0-521-86811-2, p. 127), which compete with the carbonitrides for the carbon and can influence each other, e.g. in X6CrNiMoTi17-12- 2. With the model it is possible to model the competing precipitation processes in which carbon is involved in a thermodynamically consistent manner and to control the cooling via the calculation of the (multi-component) nucleation and the growth of the precipitations in such a way that the formation of these phases is suppressed to the extent that that no Quality defects such as cracks in the semi-finished product. Although Samoilov's model has sublattices, it is not able to model the ferrite precipitation at the same time as the carbonitride, since ferrite requires a different description of the two sublattices. At least 3 sublattices are usually required to describe intermetallic phases, which applies to the sigma phase in high-alloy steels (see HL Lukas, SG Fries, B. Sundman: "Computational Thermodynamics", p. 136).

The publication of Weinzierl's model (K. Weinzierl, K. Franz, S. Schmors, "A Novel Phase Transformation Model based on Gibbs' Free Enthalpy and the Stefan Equation improves Material Properties of Hot Rolled Products" and DE 102 51 716 83 A1 ) deals only with the austenite, ferrite and cementite phases only in a hot strip cooling section. In DE 102 51 716 83 A1 it is mentioned that with the method from B. Sundman, J. Agren: "A regular solution model for phases with several components and sublattices, suitable for computer applications", J. Phys. Chem. Solids. Vol. 42, pp. 297-301, 1981, which is also the basis of the CEF, thermodynamic equilibria for multiphase systems can be calculated and the phases austenite, ferrite and cementite are given as examples. The possibility of a coupled calculation of precipitations, which influence the recrystallization and thus also the austenite transformation and the cooling effect via the grain size, in the roll stands upstream of the cooling section, is not recognized.

Proceeding from this state of the art, the invention is based on the object of optimizing a method for planning and/or controlling and/or regulating a manufacturing process in a metallurgical production plant with a plurality of successive process steps by improving the phase transformation modeling, the formation of complex chemically composed phases is predicted, which are used by material models to control the manufacturing process.

• Erstellen eines Basismodells zur Abbildung des Materialverhaltens in der Metallherstellung und -verarbeitung, insbesondere von Metallen oder metallischen Legierungen, wobei das Basismodell für Eigenschaftsbeschreibungen dieser Materialien auf die Anwendung der CALPHAD -Methode (Calculation of Phase Diagrams) zurückgreift, wobei die CALPHAD-Methode Phasen mit mehreren Untergittern (Multiple Sublattices) modelliert und dadurch thermodynamische Eigenschaften, thermophysikalische Eigenschaften und/oder davon abgeleitete Eigenschaften von metallischen_Legierungen aus dem Herstellungsprozess in der metallurgischen Anlage beschreibt,
• Erstellen von wenigstens einem Submodell, welches die Informationen des erstellten Basismodells erhält und weiterverarbeitet, wobei das Submodell Gefüge oder Werkstoffeigenschaften aus dem Herstellungsprozess in der metallurgischen Anlage beschreibt,
• Erstellen von Prozessmodellen für die mehreren aufeinanderfolgenden Prozessschritte des Herstellungsprozesses in der metallurgischen Produktionsanlage, wobei die erstellten Prozessmodelle den Herstellungsprozess des jeweiligen Prozessschrittes auf Basis eines oder mehrerer der erstellten Submodelle optimiert, und
• Optimieren des Herstellungsprozesses in der metallurgischen Anlage mit den mehreren aufeinanderfolgenden Prozessschritten unter Berücksichtigung der erstellten Prozessmodelle und globalen und/oder lokalen Optimierungszielen.

The object is achieved according to the invention by a method for planning and/or controlling and/or regulating a production process in a metallurgical production plant with a number of successive process steps, comprising the steps:

• Creation of a basic model for depicting the material behavior in metal production and processing, in particular of metals or metallic alloys, whereby the basic model for property descriptions of these materials uses the CALPHAD method (Calculation of Phase Diagrams), whereby the CALPHAD method includes phases with several sublattices (Multiple Sublattices) modeled and thereby describes thermodynamic properties, thermophysical properties and/or properties derived therefrom of metallic_alloys from the manufacturing process in the metallurgical plant,
• Creation of at least one sub-model, which receives and further processes the information of the created basic model, wherein the sub-model describes structure or material properties from the manufacturing process in the metallurgical plant,
• Creation of process models for the multiple successive process steps of the manufacturing process in the metallurgical production plant, the process models created optimizing the manufacturing process of the respective process step on the basis of one or more of the created sub-models, and
• Optimizing the manufacturing process in the metallurgical plant with the multiple successive process steps, taking into account the process models created and global and/or local optimization goals.

Planning a manufacturing process within the meaning of the invention relates to planning that is independent of the actual manufacturing process (offline), while controlling a manufacturing process relates to the actual influencing (online) of the manufacturing process.

The invention is based on the use of a universally applicable thermodynamic basic model and the sub-models based on it for material simulation in combination with process models for controlling the metallurgical manufacturing processes in a production plant, in particular steel production (also applicable e.g. to Ni, Al, Mg, Cu , Ti alloys) for the purpose of improving the quality of the semi-finished product and optimizing process control.

The basic thermodynamic model is the basis of all kinetic (sub)models for simulating phase transformations and the calculation of thermophysical properties, those of phase n shares and the chemical composition of phases is determined: enthalpy, heat capacity, density, molar volume, coefficient of thermal expansion, viscosity, thermal conductivity, electrical conductivity, magnetic properties, interfacial energies. The base model provides the submodels with data on thermodynamic equilibrium or deviations from equilibrium in metallic alloys directly or in linearized form. The basic model with the submodels (material models) is used in the process models to control the processes by deriving rules for temperature control and forming.

The basic model according to the invention can provide data for all metallurgical processes and the submodels required for them, including data for diffusion of the alloying elements ("thermodynamic factor" as a component of diffusion coefficients in addition to the atomic mobilities). According to the invention, no further thermodynamic model is required. Based on the data of a single thermodynamic base model, temperatures, heat content, deformation and structure formation (secondary, primary structure) and the resulting mechanical-technological properties can be calculated and thus used for process models and their control tasks. In other words: the thermodynamic model is part of the process model and thus part of the automation and is able to take into account the current chemical composition of the alloy.

The basic model according to the invention is the basis of all other material models for describing metallurgical processes in the course of metallurgical and manufacturing processes and is based on the thermodynamic approach of the Compound Energy Formalism (CEF) or Multiple Sublattice Model. The basic model enables continuous material modeling across multiple processes with consistent thermodynamic data. Process modeling with the associated material models begins with melting or melt treatment through to the manufacture of the semi-finished product or even the end product (e.g. forged part, additively manufactured component). This modeling makes it possible to define material properties as a controlled variable (“soft sensor”) across process stages.

According to the present invention, the thermodynamic basic model, which is based on the thermodynamic approach of the Compound Energy Formalism or Multiple Sublattice Model, works according to the CALPHAD method. The basic model represents all models for controlling processes in a process chain (e.g. solidification, hot forming, cold forming, heat treatment, ...) in the production of metals, in particular steel and Al, Ni, Ti, Cu, Mg alloys, consistent data available. Consistency not only refers to the linking of phases or phase fractions with enthalpy or specific heat capacity (which is required to simulate temperature fields), but also to the data from microstructure models in different process stages of the process chain or for competing transformations in the same process .

With conventional state-of-the-art models, there are different, simplified thermodynamic models (e.g. solubility product, substitutional solution model, Hillert-Staffansson model) for each purpose. The advantage of the invention is that only one basic model is required and the thermodynamic data for all sub-models are consistent. For example, the material models of the casting and rolling process according to the prior art are independent of each other, namely (i) for calculating thermophysical properties, (ii) for calculating precipitations and their dissolution, (iii) models for predicting austenite decay or austenitization, (iv) to calculate yield strengths taking into account precipitation, recrystallization, grain growth, and (v) to calculate solidification. In contrast, according to the invention, the thermodynamic data are consistent over the entire production process using the single thermodynamic base model. The calculation of the temperature field (thermophysical properties) and the submodel for the cast structure (transcrystalline and globulitic structure as a function of the constitutional supercooling) are carried out according to the invention with the same thermodynamic base model, since otherwise, for example, the temperature gradients required by the structure model at the solidification front are wrong.

The inventive method achieves an improvement in the quality of the metallurgical product, in particular the steel product, by improving the structure and the mechanical-technological properties by avoiding defects in the semi-finished product (e.g. cracking) by thermodynamic-kinetic modeling of the structure and the resulting phases Function of the process parameters and the chemical composition (of a melt and its scatter). The result is a much more precise description of the material behavior, especially over several process steps. This results in better control strategies for melting (e.g. setting an alloy composition, setting the casting temperature), solidification, reheating (dissolution of precipitation, heating strategies to avoid "hot shortness"/solder breakage), hot forming (rolling including thermomechanical rolling, forging, etc.), finishing (e.g. lower tolerances in the final dimensions of the product after cutting at higher temperatures through a more precise description of the thermal expansion ), heat treatment (e.g. tempering of sheet metal, long products, forgings, etc.). The thermodynamic data used, determined using the CALPHAD method, also allow extrapolation into higher-component alloy systems that are not directly supported by experimental data when determining the Gibbs energy coefficients. This is not the case, for example, with solubility products.

According to a variant according to the invention, the basic model describes the thermodynamic description of phases with several sublattices in metallic alloys, slags, gases and/or ionic crystals with electrically charged components or ionic melts. The base model may include at least two sublattices, with interstitial elements and substitutional elements residing on separate sublattices. According to an advantageous variant, the basic model also includes three or more sublattices.

• (Fe, Mn, Ni, Cr, ...)1(C,N,B,VA)1: FCC_A1
• (Fe, Mn, Ni, Cr, ...)1(C,N,B,VA)3: BCC_A2

The basic thermodynamic model is characterized by the use of several sublattices, which represent the configuration of the atoms in the crystal lattice. In the case of intermetallic phases (e.g. sigma in stainless steel), three sub-lattices are used, for carbides such as M23C6 three sub-lattices as well. In the austenite (face-centered cubic, FCC_A1 in steel or Ni alloys) and ferrite (BCC_A2) phases, two sublattices are usually used, one for the substitutional and one for the interstitial alloying elements. The sub-lattice of the interstitial sites then contains additional vacancies or vacancies VA, which are viewed like a chemical element:

• (Fe,Mn,Ni,Cr,...)1(C,N,B,VA)1: FCC_A1
• (Fe, Mn, Ni, Cr, ...)1(C,N,B,VA)3: BCC_A2

Each phase with substitutional alloying elements can contain a sublattice for interstitials and interstitial elements. These interstitial sublattices then contain an “artificial” chemical element VA (“vacancy”), which represents a lattice site that is not occupied by atoms. The sum of the sublattice fractions (“site fractions”) of all components (“constituents”: elements, molecules, ions, lattice vacancies) is 1. The molar fractions of the elements/constituents can be calculated from the sublattice fractions. Depending on the phase description, which is defined by the formula unit, the sublattices and their components, a mathematical-physical description of the Gibbs free enthalpy (Gibbs energy) is created. The thermodynamic system is defined by the intensive variables and the extensive variables. The calculation of a thermodynamic equilibrium of the system consisting of several phases, whose molar amount is given, is done by minimizing the total free energy of the system under constraints.

In a variant according to the invention, the basic model describes one or more of the following thermodynamic properties, thermophysical properties and/or properties derived therefrom in thermodynamic equilibrium, in metastable or partial equilibria: chemical potentials, Gibbs energies of phases, phase compositions, phase fractions, enthalpy, heat capacity, Density, coefficient of thermal expansion, molar volume, viscosity, thermal conductivity, electrical conductivity, magnetic properties, temperatures of phase transformations such as liquidus temperature or solidus temperature, atomic mobilities and diffusion coefficients and/or interfacial energies between phases.

According to one variant of the invention, the basic model includes all phases necessary for the manufacturing process, in particular the process steps of the manufacturing process, and their properties that are relevant due to, in particular, diffusion-controlled phase transformations in casting, hot forming, cold forming and/or heat treatment processes.

According to a preferred variant according to the invention, at least one sub-model simulates the kinetics of phase transformations and/or calculates thermodynamic properties that are determined by phase fractions and the chemical composition of phases.

In a variant of the invention, the at least one submodel describes one of the following microstructures or one of the following properties deviating from thermodynamic equilibrium: thermophysical properties, precipitation/dissolution of (intermetallic) phases, microsegregation/starting tion kinetics, defect formation (hot cracks, blowholes, pores, vacancies, dislocations), austenitization, austenite decay (austenite decay and austenitization are examples of the transformation of the matrix phase, which also occurs in titanium alloys), dendrite growth, martensitic transformations, cast structure (columnar-to-equiaxed transition). including supercooling effects of melts), hardening/softening (recovery, recrystallization), equalization of differences in concentrations and chemical potentials (diffusion, hydrogen effusion), yield stress, grain growth, formation of crystallographic textures (e.g. Goss texture in grain-oriented silicon steel), mechanical properties and/or magnetic properties such as the Curie temperature.

• • Gibbs-Energie, Enthalpie, Entropie, spezifische Wärmekapazität der Phasen und des Gesamtsystems
• • Phasenanteile (Phasenumwandlungstemperaturen), Molmengen der Phasen, Molmengen der Constituents/Elemente in den Phasen (Phasenanteile)
• • Chemische Potentiale der Elemente (constituents)
• • Chemische Zusammensetzung der Phasen (Molanteile der Elemente/Constituents) und die Zusammensetzung der Untergitter (site fractions), Partialdrücke von Komponenten in Gasgemischen
• • Diffusionskoeffizienten und thermodynamische Faktoren der Diffusion
• • Molvolumina (Dichte, Ausdehnungskoeffizient, Kompressibilität)
• • Druck eines Systems mit gasförmigen Phasen
• • chemisch treibende Kraft für die Ausscheidung einer Phase („driving force for precipitation of a new phase“)
• • Stapelfehlerenergie einer Phase (z.B. face-centered-cubic/hexagonal-closedpacked bzw. FCC_A1/HCP_A3)
• • Latente Wärmen bei Phasenumwandlungen
• • magnetische Beiträge (z.B. Curie-Temperatur, Volumenänderung bei magnetischer Umwandlung)

Based on the aforementioned models, the following results are obtained according to the invention, for example, as a result of a thermodynamic equilibrium calculation or the calculation of a metastable equilibrium (does not correspond to the state of the absolute minimum of the Gibbs energy) for the overall thermodynamic system and for each individual phase:

• • Gibbs energy, enthalpy, entropy, specific heat capacity of the phases and the total system
• • Phase proportions (phase transition temperatures), molar amounts of the phases, molar amounts of the constituents/elements in the phases (phase proportions)
• • Chemical potentials of the elements (constituents)
• • Chemical composition of the phases (molar fractions of the elements/constituents) and the composition of the sublattices (site fractions), partial pressures of components in gas mixtures
• • Diffusion coefficients and thermodynamic factors of diffusion
• • Molar volumes (density, coefficient of expansion, compressibility)
• • Pressure of a system with gaseous phases
• • chemical driving force for the precipitation of a phase ("driving force for precipitation of a new phase")
• • Stacking fault energy of a phase (e.g. face-centered-cubic/hexagonal-closedpacked or FCC_A1/HCP_A3)
• • Latent heats in phase transformations
• • magnetic contributions (e.g. Curie temperature, change in volume during magnetic conversion)

• • Viskosität von Schmelzen
• • Wärmeleitfähigkeit
• • elektrische Leitfähigkeit
• • Grenzflächenenergien zwischen Phasen

The following data can be derived directly from these thermodynamic data as a function of the phase fractions and phase composition:

• • Viscosity of melts
• • Thermal conductivity
• • electric conductivity
• • Interfacial energies between phases

In addition, partial thermodynamic equilibria are formulated and calculated under certain constraints, such as the para-equilibrium in steel (PEQ) of ferrite, austenite, cementite in steel: The substitutional elements that make up the main element (e.g. Fe in steel, C on interstitial (Intermediate) lattice site) on its lattice site, assumed to be stationary and combined into a virtual chemical element Z.

A special form of the equilibrium calculation under constraints - namely the condition that the concentration of a product phase after the transformation is taken over by the matrix phase. Such a partition-free transformation is possible from the so-called T0 temperature, from which the Gibbs energies of the phases involved are the same with unchanged concentration, see also M. Hillert: "Phase Equilibria, Phase Diagrams and Phase Transformations, Their Thermodynamic Basis", Cambridge University Press, ISBN 978-0-521-85351-4, PEQ: p. 311.

In a preferred variant of the invention, data is exchanged between the base model, the multiple sub-models and/or the multiple process models. The models mentioned communicate work with each other in order to optimize the manufacturing process in the metallurgical production plant with several consecutive process steps.

According to a variant according to the invention, one or more process models determine control variables and/or manipulated variables for the automation of the production process in order to optimize an assigned process step of the production process.

According to one variant of the invention, the controlled variables are selected from: viscosity of the melt, phase fraction, in particular solid phase fraction and melt fraction, data on precipitations, in particular their type, volume fraction, mean size and/or distribution function of the size of the precipitations, mean grain sizes and/or distribution function of the Grain sizes, proportions of microstructure (e.g. bainite, pearlite), dislocation density, mechanical properties, formability, formability, Cottrell clouds, degree of homogenization, Zener pinning force (Zener force, G. Gottstein: "Materials Science and Engineering, Physical Fundamentals", Springer, ISBN 978-3-642-36602-4, p. 356), overheating of the melt at the start of casting until the end of casting, position of solidification, strand shell thickness, strand surface temperatures, position of the specified phase parts, drawing temperature, dissolution of the precipitates, distribution of the austenite grain size, dimensions and geometry of semi-finished product/end product (e.g. additively manufactured, forged, ...), final rolling temperature, microstructure before forming, drawing temperature from the reheating process, mass flow, rolling speed, water volume for interstand cooling and/or coiler temperature.

In a variant according to the invention, the manipulated variables relate to process parameters such as temperature, time, speed, pressure, force, volume flows, geometry and the like and are selected in particular from: tapping temperature of the melt, heating power, mass of cooling scrap, flushing time, casting speed, water quantities and air pressure of the secondary cooling, temperature and residence time in heating or cooling zones, application temperature and/or degree of deformation.

According to a preferred variant of the invention, the several successive process steps of the manufacturing process are selected from a group of features formed from: melting and alloying, ingot casting, continuous casting, finishing (slabs, sheets, long products, rings), reheating, hot rolling, cold rolling, heat treatment, surface coating , surface hardening, extrusion, forging, transportation, tube making, coating in strip treatment and/or additive manufacturing.

According to an expedient variant of the invention, the global optimization goals relate to the entire production process in the metallurgical production plant.

In a variant according to the invention, the local optimization goals relate to one or more of the process steps of the production process in the metallurgical production plant.

According to an advantageous variant of the invention, the optimization goals are selected from a group of features formed from: product quality, product defects, chemical product composition, energy consumption, production rate, machine utilization, machine wear and/or production time.

According to one variant of the invention, several sub-models are created, each of which receives and further processes the information from the base model created, with each sub-model describing microstructure or material properties from the manufacturing process in the metallurgical plant.

In a variant according to the invention, the process model provides information about the temperature control, the mass flow and/or the forming in order to optimize the manufacturing process.

According to an advantageous variant of the invention, in addition to the chemical composition and the temperature, calculation results of the sub-models and the process models are also transferred to the base model, so that the results of the base model are specified and transferred back to the sub-models in order to carry out an iterative calculation.

According to an expedient variant of the invention, the basic model comprises two or more sublattices, one sublattice containing the interstitial components ("constituents", e.g. chemical elements) with lattice gaps ("vacancies") and the other sublattice containing the substitutionally dissolved components.

• • Bildung und Auflösen von Phasen und Ausscheidungen
• • Phasenanteile
• • Gefügeanteile
• • Korngröße
• • Ausgleich von Konzentrationsunterschieden
• • Entstehung von Fehlstellen (Lunker, Poren im Walzgut, Versetzungsdichte)
• • .. usw.

The process control takes place in particular on one or on a combination of the following material properties at a defined point in the product, process or in the system:

• • Formation and dissolution of phases and precipitates
• • Phase components
• • Structural parts
• • grain size
• • Compensation for differences in concentration
• • Creation of defects (shrinkage cavities, pores in the rolling stock, dislocation density)
• • .. etc.

• • Viskosität einer Schmelze während des Gießens
• • Festphasenanteil („fraction solid“) beim Auslauf aus der Gießmaschine
• • Festphasenanteil bei Soft- und Hard-Reduction
• • Ausscheidungszustand von Phasen in Bereichen mit mechanischer Belastung
• • Primärgefüge (Anteile an globulitischen und transkristallinen Körnern)
• • Sekundärgefüge Eisenwerkstoffe (Anteile Austenit, Ferrit, Perlit, Bainit, Martensit, Zementit, Graphit)
• • Korngröße
• • Versetzungsdichte
• • rekristallisierter und erholter Anteil („recovery“) bzw. entfestigter Anteil („softening fraction“)
• • Umformfestigkeit bzw. Fließspannung (wahre Spannung)
• • Konzentrationsunterschiede bzw. Homogensierungsgrad (z.B. Seigerungen)
• • Ferromagnetismus (Stahl: Anteile an kubisch raumzentrierten Phasen)

Such material properties or structural components as control variables are, for example:

• • Viscosity of a melt during casting
• • Fraction solid when leaving the casting machine
• • Solid phase component with soft and hard reduction
• • State of precipitation of phases in areas with mechanical stress
• • Primary structure (portions of globulitic and transcrystalline grains)
• • Secondary structure of iron materials (proportions of austenite, ferrite, pearlite, bainite, martensite, cementite, graphite)
• • grain size
• • Dislocation density
• • Recrystallized and recovered portion (“recovery”) or softened portion (“softening fraction”)
• • Yield strength or yield stress (true stress)
• • Differences in concentration or degree of homogenization (e.g. segregation)
• • Ferromagnetism (steel: portions of body-centered cubic phases)

The invention is explained in more detail below using exemplary embodiments.

material models

Material models are required to calculate the material properties or structural components. All of these material models require thermodynamic material data, all of which are calculated using the basic thermodynamic model as a function of chemical composition, temperature and pressure. All material and microstructure models are therefore linked to the thermodynamic basic model. Material model for material property, microstructure Type of thermodynamic equilibrium data Model result (so-called soft sensors) Melt/slag/gas phase Phase proportions, phase compositions, partial pressures Phase fractions, chemical compositions, partial pressures, liquidus and solidus temperatures viscosity of the melt Phase proportions, phase compositions viscosity of the melt fraction solid Phase fractions, phase compositions, chemical potentials, thermodynamic factors, diffusion coefficients, molar volume, interfacial energies Phase fractions, segregation profiles, enthalpy, latent heat precipitation state (+ matrix phase state) Phase fractions, phase compositions, chemical potentials, thermodynamic factors, diffusion coefficients, molar volume Precipitation size distribution function, phase compositions matrix+precipitations primary structure Phase diagram (liquid slope, element partition coefficients), thermodynamic factors, diffusion coefficients, molar volume, latent heat Volume fraction of globulitic structure, supercooling of the solidification front, primary and secondary dendrite arm spacing secondary structure Phase fractions, phase compositions, chemical potentials, thermodynamic factors, diffusion coefficients, molar volume during austenite transformation (formation and dissolution of austenite, ferrite, pearlite, bainite, martensite) Transformation temperatures (also dependent on the degree of deformation), phase and microstructure proportions hot cracks Phase fractions, phase compositions, density of melt and solid phases, microsegregation Brittle Temperature Range (BTR), Zero Ductility Temperature (ZDT), Zero Strength Temperature (ZST), Liquid Impenetrable Temperature (LIT), thermal contraction Forming strength including hardening and softening (recrystallization, recovery), mechanical properties of the end product (yield point, elongation at break, toughness) Precipitation state, diffusion coefficients, phase fractions, phase compositions, phase transition temperature (also as a function of the degree of deformation) Dislocation density, zener pinning force, solute drag force, softened portion (recrystallized and recovered portion), recrystallized grain size, transformation temperature (e.g. Ae3 on steel) grain growth Precipitation state (Zener pinning force), diffusion coefficients, phase fractions, phase compositions, solute drag Grain size distribution (incl. mean, variance), texture Diffusive equalization (gradient concentration, chemical potentials) Precipitation state (dispersed phase matrix), diffusion coefficients, phase fractions, phase compositions Concentration field, element concentration, phase state, degree of homogenization (incl. edge decarburization, carburization) scale (scaling / oxidation) Phase fractions, phase compositions, chemical potentials, thermodynamic factors, diffusion coefficients Scale layer thickness and composition

The mechanical properties of the end product depend on solid solution and strain hardening, as well as precipitation and fine-grain hardening. The material model for calculating these mechanisms of strength increase in turn requires information from the submodels and thus from the thermodynamic base model.

Process models, submodels and controlled variables

The process models are required to simulate the process and to control or regulate the material properties. Process parameters such as temperature, time, speed, pressure, force and volume flows are available as manipulated variables for achieving the desired material properties and product geometry. These process models also require thermodynamic data from the Material properties or the structure are dependent and are made available by the material models. process model Material or structure model controlled variable Melt thermophysical properties Viscosity and melt temperature melt treatment thermophysical properties, alloy model Chemical composition of melt and slag, temperature continuous casting Microsegregation, diffusive equalization, state of precipitation, hot cracking, secondary structure, grain growth, scale Phase proportion + size distribution precipitations, grain sizes, microstructure proportions ferrite, austenite, bainite, pearlite, martensite, cementite, melt viscosity Cooling cast product (slab, bloom, billet, round, blank, ingot cast ingot) Precipitation state, diffusive equalization, secondary structure, grain growth, scale, coefficient of thermal expansion Phase proportion + size distribution of precipitations, grain sizes, microstructural proportions of ferrite, austenite, bainite, pearlite, martensite, product dimensions (e.g. cold thickness) rewarming (including: inductive, conductive) Diffusive Balance, Precipitation State, Secondary Structure, Grain Growth, Scale, Curie Temperature Phase proportion + size distribution Precipitations, grain sizes, microstructural proportions ferrite, austenite, bainite, pearlite, martensite Hot forming (rolling, forging, extrusion) Precipitation state, secondary structure, grain growth, formability & softening, scale Phase proportion + size distribution Precipitations, grain sizes, microstructure proportions ferrite, austenite, bainite, pearlite, martensite, dislocation density, mechanical properties, product dimensions (e.g. cold thickness) Cooling after hot forming (interstand cooling, water cooling section, coiler, sheet stack, delayed cooling in hoods/pits, wire cooling with air) Secondary structure, state of precipitation, grain growth, yield strength & softening, scale Phase proportion + size distribution Precipitations, grain sizes, microstructure proportions ferrite, austenite, bainite, pearlite, martensite, dislocation density, mechanical properties, product dimensions (e.g. cold thickness) cold forming Forming strength, state of precipitation, secondary structure Dislocation density, formability, Cottrell clouds (C, N) Heat treatment (strips, sheets, coils, long products, forgings, extruded profiles and piece goods) Secondary structure, state of precipitation, grain growth, yield strength & softening, scale, diffusive compensation Phase proportion + size distribution Precipitations, grain sizes, microstructure proportions ferrite, austenite, bainite, pearlite, martensite, dislocation density, degree of homogenization, mechanical properties, product dimensions (e.g cold thickness) Surface treatment (e.g. hot-dip galvanizing, bright annealing of stainless steel with hydrogen), chemical surface treatment (carburizing, carbonitriding, nitriding, boriding) Scale/Oxidation, Diffused Equalization Phase fraction + size distribution Precipitations, grain sizes, proportions of microstructure ferrite, austenite, bainite, pearlite, martensite, dislocation density, degree of homogenization, mechanical properties

Process chains (examples)

• CSP (Compact Strip Production)
• Stahl, Konventionell Grobblech: + Vergüten
• Stahl, Konventionell Warmband: + Rohrschweißen (API)
• Stahl, Konventionell Warmband: + Kaltband + Bandbehandlung (WB) + Dressieren
• Stahl, Konventionell Warmband: + Kaltband + Haubenglühen
• Langprodukte Träger, Stabstahl, Draht, Rohre (nahtlos), Schmiedeteile: + Wärmebehandlung
• Aluminium: Barren-Strangguss/Blockguss - Wiedererwärmen - Warmwalzen - Bandbehandlung (Schwebebandofen: Homogenisierung und Abschrecken)
• Twin-Roll Casting + Warmumformung (Warmwalzen)

The following process chains for steels and non-ferrous metals all begin with the steps melting + casting + cooling cast product - reheating - hot forming - cooling after hot forming:

• CSP (Compact Strip Production)
• Steel, conventional heavy plate: + quenching and tempering
• Steel, Conventional Hot Strip: + Tube Welding (API)
• Steel, conventional hot-rolled: + cold-rolled strip + strip treatment (WB) + skin-passing
• Steel, conventional hot strip: + cold strip + batch annealing
• Long products Beams, bars, wire, tubes (seamless), forgings: + heat treatment
• Aluminum: ingot continuous casting/ingot casting - reheating - hot rolling - strip treatment (flotation furnace: homogenization and quenching)
• Twin-Roll Casting + Hot Forming (Hot Rolling)

Examples for the application of the material models in the process chains and their control variables

Based on the thermodynamic base model, the data is made available to all process models and the material models of the process chains. Furthermore, all calculated material data are made available to the automation or adaptation as "soft sensors" (soft, smart or virtual sensor: the calculated material parameter is treated like a measured value), so that the data can also be processed with "Machine Learning” methods can be used. These would be, for example, in the processes:

Melting and alloying:

• Setting the alloy composition of the melt:
  • • Alloy composition
    • ◯ The basic model is used to calculate the chemical composition of the melt, which is influenced by the addition of alloying agents and the transfer of alloying agents into the slag.
    • ◯ When alloying the melt, a chemical reaction (endothermic or exothermic) or a change in the phase state usually takes place, which leads to a change in temperature. The basic model provides the thermodynamic data for calculating this temperature change.
• Adjusting the melt for casting:
  • • Viscosity melt
    • ◯ The goal is to find an optimum between castability, internal quality (central segregation, primary structure) and energy saving. An important criterion is the safety of the casting process: if, for example, the melt is too hot, there is a risk of breakthrough; if the melt is too cold, solidification can begin above the mould. The temperature is usually used as the controlled variable. An improvement can be achieved by additional use of viscosity, especially when casting with low superheat.

continuous casting:

• Determination of the following parameters as controlled variable:
  • • Solid phase fraction / melt fraction and temperatures derived from it, such as zero toughness (ZDT, zero ductility temperature), zero strength (ZST, zero strength temperature), zero make-up (LIT, liquid impenetrable temperature)
    • ◯ Setting the position of solidification or the positions of ZDT, ZST, LIT in the continuous caster, as well as the position of soft reduction and hard reduction.
  • • Zener pinning force (calculated as a function of volume fraction and size of precipitates)
    • ◯ Minimization of surface cracks (minimum ductility) in areas of the plant with mechanical stress on the strand shell (bending, straightening, bulging due to metallostatic load).
  • • Ferrite phase component
    • ◯ Minimization of surface cracks (minimum ductility) in areas of the system with mechanical stress on the strand shell (bending, straightening, bulging due to metallostatic load) and for austenite grain refinement in order to avoid solder cracks/hot shortness (elements such as copper that trigger solder cracks are distributed over a larger specific grain boundary area per unit volume, resulting in smaller crack depth).
  • • Martensite start temperature and fraction
    • ◯ Minimization of surface cracks, especially in cooling concepts (with comparatively high amounts of water, which can lead to a significant drop in surface temperature close to the martensite start temperature:
      • a) thermal soft reduction to reduce central segregation, and b) austenite grain refinement through ferrite formation and reconversion to austenite to avoid solder fracture/hot shortness.
    • ◯ Avoidance of martensite in the core of slabs, e.g. in tubular steel, by reducing central segregation using soft reduction
  • • Share of globular primary structure (equiaxed)
    • ◯ Maximization of the equiaxed volume fraction in the slab cross-section to reduce central segregation
    • ◯ Maximization of the equiaxed volume fraction to reduce the risk of internal cracks (e.g. with soft reduction)
    • ◯ Maximizing the equiaxed volume fraction to reduce ridging (roping) in ferritic stainless steels.
  • • Critical strain or strain rate for internal or hot cracking (with the help of a "hot-tearing" model, e.g. Y.-M. Won et al.: "A New Criterion for Internal Crack Formation in Continuously Cast Steels", Met. Trans . B, Vol. 31 B, (2000), p. 779)
    • ◯ Internal cracking in transcrystalline (columnar) primary structure.

Transport between casting and reheating:

• Determination of the following parameters as controlled variable:
  • • Zener pinning force (calculated as a function of volume fraction and size of precipitates) to minimize surface cracks (ductility minimum) in areas of the system with mechanical stress on the casting.
  • • Ferrite phase component
    • ◯ Minimization of surface cracks (ductility minimum) in areas of the system with mechanical stress on the cast part.
    • ◯ Minimization of internal stresses at grain boundaries due to transformations into phases with different densities that start there.
  • • Martensite content (starting temperature)
    • ◯ to minimize surface cracks (cold cracks)
    • ◯ Avoidance of martensite formation in the core of castings through delayed cooling
  • • Hydrogen concentration to avoid flake cracks (delayed fracture), e.g. when stacking slabs (later in production also valid for sheets)

rewarming:

• Determination of the following parameters as controlled variable:
  • • Austenite and ferrite phase proportions:
    • ◯ The subsequent hot forming of steel should take place in the area of austenite.
    • ◯ Grain-oriented silicon steel is reheated in the temperature range in which both ferrite and austenite become stable when heated. In the presence of austenite, control phases (precipitations to control grain growth and form the Goss texture) dissolve better, since the solubility in austenite is higher.
  • • Phase fractions precipitations:
    • ◯ In the case of grain-oriented silicon steel, the control phases (ie the elements Mn, S, Al, N, ... of the control phases MnS, CuS, AlN, ...) should be dissolved as completely as possible to set the Goss texture.
    • ◯ Complete dissolution of precipitates of the micro-alloying elements before thermomechanical rolling.
    • ◯ Dissolution of precipitates from forged parts and extruded profiles (e.g. heat-treatable aluminum alloys)
  • • Zener Pinning Force (ZPF) to control grain growth:
    • ◯ Avoidance of incomplete or partial dissolution of precipitations of the micro-alloying elements (Ti, V and especially Nb), which leads to abnormal grain growth and thus to inhomogeneous grain size, which impairs the toughness properties (DWTT for pipe steels). This occurs when the grain growth driving force is approximately equal to the ZPF.
    • ◯ Setting a ZPF that is greater than the driving force of grain growth and leads to inhibition of grain growth, e.g. by alloying Ti or Nb in micro-alloyed steels.
  • • Grain size (statistical parameters of grain size distribution)
    • ◯ The grain size distribution influences the mechanical properties of the end product (e.g. DWTT for pipe steel).
    • ◯ A finer grain on the surface reduces the crack depth in hot shortness of steels with an increased Cu content.
  • • resistance to deformation
    • ◯ Setting a low formability with simultaneous reduction of the process temperatures to save energy, reduce scale formation, shorten the heating times and increase the throughput. A low yield strength for less equipment stress and wear.
  • • Degree of homogenization or distribution of the alloying elements in the material volume
    • ◯ Compensation of segregations.

hot stamping:

• Determination of the following parameters as controlled variable:
  • • Austenite and ferrite phase proportions:
    • ◯ During the hot rolling (strip) of ferritic steels (e.g. soft steels for cold forming), ferrite must be avoided in order to avoid coarse grains, particularly near the surface and near the edges of hot strip.
    • ◯ Hot rolling (sheet, strip): The goal is a phase state that is homogeneous in the cross section and along the length. An inhomogeneous state leads to locally different physical properties. For example, non-uniform strengths across the width or cross-section lead to inhomogeneous forming and thus to implanarity, e.g. X, p. 23).
    • ◯ Hot rolling (sheet, strip): The goal is a phase state that is homogeneous in cross section and length in order to minimize crop losses
    • ◯ Hot rolling, thermomechanical treatment (TMB) of micro-alloyed steels:
      • The aim of the TMB is to set a grain that is as fine as possible after the austenite decay. This can be generated by the fact that the recrystallization of the austenite and the austenite decomposition take place practically simultaneously. For this purpose, the last forming step should take place as close as possible to the temperature at which a small proportion of ferrite (<5%) has formed.
  • • Martensite phase fraction
    • ◯ During hot rolling of slow-transformation steels, it must be avoided that the interstand cooling is so intense that the temperature falls below the martensite start temperature and quench hardening occurs.
    • ◯ Avoidance of martensite in the core of slabs, eg in tubular steel
  • • Solidus temperature / phase fraction melt
    • ◯ Rolling of long products: Due to the high forming speeds, the forming heat cannot be dissipated, so that volumes in the product could begin to melt.
  • • Zener pinning force (phase components and size distribution) eliminations
    • ◯ Hot rolling, thermomechanical treatment (TMB) of micro-alloyed steels: Precipitations influence the recrystallization kinetics and exert a restoring force on grain boundaries.
  • • Concentration element in phase / chemical composition phase:
    • ◯ Hot rolling, thermomechanical treatment (TMB) of micro-alloyed steels:
      • The micro-alloying elements dissolved in austenite (especially Nb) have a recrystallization-retarding effect. To do this, a minimum salary must be paid or not eliminated.
  • • Grain size distribution
    • ◯ After recrystallization, grain growth takes place, which can be influenced by the temperature and the precipitation state.
  • • Forming strength:
    • ◯ All hot forming processes: The forming strength depends, for example, on the phase state and the dislocation density and must be known in order to calculate the necessary forming forces.
  • • Recrystallized or softened portion
    • ◯ Hot rolling, thermomechanical treatment (TMB) of micro-alloyed steels:
      • The aim of the TMB is to set a grain that is as fine as possible after the austenite decay. This can be achieved if the multi-stage forming is divided in such a way that dynamic recrystallization takes place in the last pass.

Cooling after hot forming:

• Determination of the following parameters as controlled variable:
  • • Phase fractions and size distribution precipitations:
    • When hot-rolling precipitation-hardening steels (e.g. micro-alloyed pipe steels), precipitation hardening should take place in the hot-rolled strip coil by selecting the coiling temperature in order to set the required yield point. For cold-forming steels sold in the batch furnace annealed condition (soft, texture with good deep-drawing properties), aluminum nitride precipitation in the hot-rolled coil must be avoided.
  • • grain size
    • ◯ The grain size influences the yield point (fine grain hardening, Hall-Petch relationship, see book quote above: G. Gottstein, ...)
  • • Martensite content
    • ◯ Quench & self-tempering of sheet metal, wire and bar steel: Targeted transformation of the surface into martensite, which is tempered by the core heat.
    • ◯ Hardening of the rail head: The aim is to achieve the finest possible pearlitic structure, which has a high level of wear resistance. Martensite and bainite should be avoided.
  • • Secondary cementite content
    • ◯ Minimization of (brittle, hard) carbide networks in hypereutectoid steels, which otherwise have to be dissolved by subsequent heat treatment (soft annealing, GKZ annealing = annealing on nodular cementite).
  • • Mechanical properties (e.g. yield point)
    • ◯ The mechanical properties depend on the metallurgical hardening mechanisms (fine-grain hardening, precipitation hardening, work hardening through dislocations, solid solution hardening). The base model provides the thermodynamic data necessary to calculate these hardening mechanisms.

heat treatment:

Similar parameters as controlled variables can also be defined for the heat treatment and surface coating of sheets, strips and long products, since the metallurgical hardening mechanisms listed above also take place there and thus also the methods for setting mechanical properties (yield point, tensile strength, elongation at break, notched bar impact work, reduction in area at break). , r-value, hardness) are applicable.

• • Ausscheidungszustand (Volumenanteil, Verteilungsfunktion der Größe, Zener-Pinning Force)
  • o Bei der Wärmebehandlung von kornorientierten Siliziumstählen wird mit der Hilfe von sogenannten Steuerphasen (Ausscheidungen, z.B. AIN, MnS, ...) abnormales Kornwachstum und damit die Goss-Textur eingestellt. Dieses wiederum bedingt, dass die ferritischen Körner die Goss-Textur aufweisen. Diese führt dazu, dass die Ummagnetisierungsverluste beim Einsatz in Transformatoren minimiert werden.
  • ◯ Wärmebehandlung von Blech: Gezielte Steuerung der Kühlung derart, dass die Ausscheidung von Phasen (z.B. Karbide) minimiert wird oder gleichmäßig über die Dicke bzw. den Querschnitt stattfindet zwecks Einstellung homogener Bedingungen zur Bildung von Martensit.
• • Martensit-Anteil
  • ◯ Wärmebehandlung von Blech, Draht und Stabstahl: Gezielte Steuerung der Kühlung so, dass die Umwandlung in Martensit über den Querschnitt annähernd gleichzeitig abläuft, um Planheitsdefekte und Eigenspannungen im Produkt zu minimieren.
  • ◯ Wärmebehandlung von Band aus Mehrphasenstahl (z.B. Quench & Partitioning Stahl): Gezielte Steuerung des Anteils an Austenit und Martensit nach dem Abschrecken durch modellgestützte Ermittlung des Martensitanteils, u.a. als Funktion von chemischer Zusammensetzung, Temperatur-Zeit-Führung, Phasenanteile vor dem Abschrecken und der Austenitkorngröße. Auswahl einer geeigneten Temperatur des Kohlenstoff-Partitioning (Partitioning: Umverteilung von Kohlenstoff von Martensit in Austenit zur Stabilisierung des Austenits bei Raumtemperatur) durch Vermeidung der Bildung von Bainit und von Karbiden.

Determination of the following parameters as controlled variable:

• • Precipitation state (volume fraction, size distribution function, Zener pinning force)
  • o During the heat treatment of grain-oriented silicon steels, abnormal grain growth and thus the Goss texture are adjusted with the help of so-called control phases (precipitations, eg AlN, MnS, ...). This in turn causes the ferritic grains to have the Goss texture. This means that core losses are minimized when used in transformers.
  • ◯ Heat treatment of sheet metal: Targeted control of cooling in such a way that the precipitation of phases (e.g. carbides) is minimized or takes place uniformly across the thickness or cross-section in order to set homogeneous conditions for the formation of martensite.
• • Martensite content
  • ◯ Heat treatment of sheet metal, wire and bar steel: Targeted control of cooling in such a way that the transformation into martensite takes place almost simultaneously across the cross-section in order to minimize flatness defects and residual stresses in the product.
  • ◯ Heat treatment of strip made of multi-phase steel (e.g. quench & partitioning steel): Targeted control of the proportion of austenite and martensite after quenching through model-based determination of the proportion of martensite, including as a function of chemical composition, temperature-time control, phase proportions before quenching and the austenite grain size. Choosing a suitable carbon partitioning temperature (partitioning: redistribution of carbon from martensite to austenite to stabilize austenite at room temperature) by avoiding the formation of bainite and carbides.

The content of chemical elements in the material volume can be used as a control variable in all process stages. These contents can change as a result of diffusion processes (e.g. of C, N, B) in the semi-finished product, e.g. in the case of (undesirable) edge decarburization or the desired effusion of hydrogen to avoid hydrogen embrittlement (flake cracks, delayed fracture). During the effusion of hydrogen, the basic model and a sub-model showing the proportions of microstructure (esp. austenite, ferrite and bainite) as a function of the temperature-time profile can be used to calculate the optimum temperature range, taking into account the diffusion of H can be determined in which the effusion speed is maximum. The temperature must be as high as possible and just below the Ac1 temperature at which austenite forms, which has a high solubility for H and thus hinders effusion.

The same principle of "element content as a control variable" also applies to surface hardening processes in which chemical elements are introduced into the material surface, e.g. in targeted carburizing to set a desired hardening depth, in nitriding (and the formation of nitrides), in carbonitriding and boriding of steels.

improvement of the state of the art

The state of the art is improved in that a thermodynamic base model supplies data from a metal-physically/thermodynamically consistent database to several downstream sub-models, which also work across process stages (process chain) and are used to control or regulate material properties and quality parameters in combination with process models. Furthermore, the process models are provided with thermophysical properties that are required to control or regulate the processes. The basic model is characterized by the fact that it works according to the "Compound Energy Formalism" (CEF) method, which also describes phases with more than two sublattices. In the general case, the sublattices contain constituents, which include chemical elements, molecules, or ions. Their fractions (“site fractions”) on each sublattice add up to 1. The mole fractions can be calculated from the “site fractions”. It is also characterized by the fact that phases such as austenite and ferrite in steel (2 sublattices each) are described with both substitutional and interstitial sublattices, with the latter treating the unoccupied lattice gaps like a chemical element (VA, "Vacancies"). This enables the phase to be modeled over a significantly wider range of chemical composition than modeling the interstitial elements (e.g. C, N in steel, cast iron, nickel-based alloys, phases: austenite, ferrite) together with the substitutional elements with only one sublattice . This also enables the calculation of high-carbon steels such as the tool steel X210CrW12 (1.2436), which also forms carbide types in addition to the high carbon content, which can only be modeled meaningfully with more than 2 sublattices. It also enables the simultaneous calculation of face-centered cubic carbonitrides of the elements Nb, V, Ti, which are formed simultaneously with body-centered cubic ferrite from face-centered cubic austenite.

The consistent thermodynamic data calculated with the base model can be used to calculate thermophysical properties on the one hand, which can then be used to calculate the time-dependent development of temperature fields and the dependent change in geometry in semi-finished metal products. With the basic model, all sub-models (material models) in a process chain for simulating the development of phase transformations over time and the influence on the material structure are supplied with thermodynamic data. These include, for example, the sub-models: thermophysical properties, calculation of the kinetics of phase transformations such as solidification, cast structure (transcrystalline lobulitic), austenite transformation, precipitation of intermetallic phases, grain growth, recrystallization, diffusion compensation of chemical components.

Example process chain: continuous casting, hot transport of semi-finished products, reheating of steel taking into account the interaction of microsegregations, (carbonitride) precipitation - austenite transformation (e.g. ferrite formation).

Microsegregations can be calculated using a Scheil-Gulliver model, for example, in which the thermodynamic equilibrium data are calculated using the thermodynamic base model. The result is a segregation profile of all chemical elements (mole fraction of element j: x _j ) as a function of a characteristic length z (eg secondary dendrite arm distance): x _j (z). At each point of the calculated segregation profile z, the chemical composition x _j (z) can be used to model the austenite transformation and precipitation processes (e.g. of carbonitrides of the micro-alloying elements Nb, V, Ti, in steel or of W in Ni-Leg. , aluminum nitride, manganese, iron sulfide), especially in interdendritic areas and at grain boundaries. These metallurgical processes can have an embrittling effect and lead to cracks in the cast product. In order to avoid these phase transformations, the cooling can be dynamically adjusted online.

Example process chain: Hot forming with subsequent cooling: The change in energy during forming processes (e.g. rolling, forging, extrusion), which results in a change in the dislocation density in the material structure, has an influence on phase transformations and is taken into account by an additional term of the Gibbs energy in the basic model. Supplying different sub-models via a process chain is only possible if the complete multi-component and multi-phase system is taken into account, which can only be ensured by using phases with more than two sublattices.

Furthermore, with the basic thermodynamic model used according to the invention, it is even possible to describe a para-equilibrium (PEQ, only the interstitial elements are in thermodynamic equilibrium) as a multi-component and multi-phase system with several interstitial elements (C, N, ...) to calculate. The interstitial elements are usually involved in the formation of carbonitride (especially Nb, Ti, V) as well as in the austenite transformation. This means that an equilibrium with austenite, ferrite and cementite, which is usually modeled with a PEQ, can also be calculated. This is used in a hot strip cooling section with simultaneous carbonitride precipitation, which has already started in the upstream rolling mill during forming. Furthermore, the precipitation of carbonitrides during thermomechanical rolling of microalloyed steels affects the dislocation density and recrystallization in the austenite, which in turn changes its free energy, which in turn has an impact on ferrite formation. The interactions of rolling force-deformation-dislocation density and carbonitride precipitation-dislocation density and austenite transformation-dislocation density are preferably calculated iteratively. The base model, the sub-models and the process models are used for these iterative calculations.

The formation of martensite in steels can be calculated using the concept of the T0 temperature. In addition to the chemical contributions, other contributions such as mechanical energy contributions can also be added to the Gibbs energy descriptions of the phases that convert without partitions. The martensite start temperature, the driving force for martensite formation and the changes in enthalpy associated with martensite formation are provided by the base model and used by the sub-models and the process models. For example, it can be predicted what influence the precipitation of carbides has on the carbon content of the austenite, which in turn influences the martensite start temperature.

The method according to the invention provides, e.g. for the stainless steel X6CrNiMoTi17-12-2, all phases necessary for the process chain and their properties, which are relevant due to diffusion-controlled phase transformations in melting, casting, hot forming and heat treatment and surface treatment processes. This concerns, among other things, the precipitation of the carbonitrides (Ti)(N,C), (Nb)(C,N), the M23C6 carbide and the sigma phase. These precipitations are relevant to quality and can be analyzed using the basic model in combination with the sub-models in simulations to control the cooling, e.g. in a continuous casting plant or in solution annealing to avoid the precipitation of the chromium-rich carbide M23C6 and the brittle sigma phase through online dynamic calculation of the nucleation and growth of these phases can be calculated. The data of the phases are calculated thermodynamically consistent, even if the change in the state of one phase (e.g. sigma) affects the state of the other phases (e.g. austenite).

Fields of Application of the Invention

• 1.1. Stahlerzeugung,
• 1.2. Stranggießen (Bramme, Knüppel, Vorblock, Beam-Blank, Rund, Band, Block)
• 1.3. Brammen-, Blech- und Langproduktadjustage
• 1.4. Warmwalzen (Wiedererwärmen, Vor- und Fertigwalzen, Wasser-/Luftkühlung, Warmbandhaspeln, Kühlbett,Windungskühltransport)
• 1.5. Kaltwalzen
• 1.6. Wärmebehandlung (Vergüten von Grobblech, Band aus Mehrphasenstählen in „Continuous Annealing/Galvanizing Line“)
• 1.7. Schmieden
• 1.8. Regeln für den Transport (z.B. mittels Kran) von Halbzeug.
• 1.9. Strangpressen z.B. von Aluminiumlegierungen

Production of semi-finished products made of steel and non-ferrous metals (long and flat products, forged products, extruded profiles) and components from additive manufacturing: definition of process parameters and control of the process, taking into account the 1st structure formation

• 1.1. steelmaking,
• 1.2. Continuous casting (slab, billet, bloom, beam blank, round, strip, block)
• 1.3. Slab, sheet and long product finishing line
• 1.4. Hot rolling (reheating, roughing and finish rolling, water/air cooling, hot strip coilers, cooling bed, coil cooling transport)
• 1.5. cold rolling
• 1.6. Heat treatment (tempering of heavy plate, multi-phase steel strip in continuous annealing/galvanizing line)
• 1.7. Forge
• 1.8. Rules for the transport (e.g. by crane) of semi-finished products.
• 1.9. Extrusion of eg aluminum alloys

• 2.1. Verbesserung der Qualität von Halbzeugen durch Verbesserung des Gefüges und der mechanisch-technologischen Eigenschaften zur Vermeidung von Fehlern am Halbzeug (z.B. Rissbildung) durch thermodynamisch-kinetische Modellierung des Gefüges und der entstehenden Phasen als Funktion der Prozessparameter und der chemischen Zusammensetzung (einer Schmelze und deren Streuungen):
  • 2.1.1. Stahlerzeugung: Berechnung thermophysikalischer Eigenschaften der Schmelze
  • 2.1.2. Stranggießen einschließlich Twin-Roll Casting, (Twin-) Belt Casting, Block Casting, Continuous Ingot Casting:
    • 2.1.2.1. Vorhersage der Rissempfindlichkeit und Vermeidung von Rissen
      • - Vermeidung von Heißrissen: Vorhersage der Nullzähigkeitstemperatur, der thermischen Kontraktion der festen Phasen im rissempfindlichen Temperaturintervall
      • - Vermeidung von ausscheidungsinduzierten Oberflächenrissen:
        • (chemisch treibende Kraft der Keimbildung von Ausscheidungen auf Korngrenzen und im Korn)
      • - Vermeidung von Ferritfilmen auf Korngrenzen: (chemisch treibende Kraft der Keimbildung von Ferrit auf Korngrenzen und im Korn)
      • - Minimierung des Effekts von versprödenden intermetallischen Phasen (z.B. Sigma- oder Laves-Phase) in Stranggussprodukten
      • - Kornfeinung (oberflächennah)
      • - Kernaufreißungen oder Delaminationen durch Flüssigkeitsfilme auf Korngrenzen.
      • - Oberflächenrissvermeidung bei Intensivkühlung (Oberflächentemperaturen sind im Bereich der Phasenumwandlung) für „thermische Soft-Reduction“
    • 2.1.2.2. Verringerung Makroseigerungen und Karbidzeiligkeit im Kern eines Gussproduktes
    • 2.1.2.3. Einstellung eines Gussgefüges mit definiertem Anteil globulitischen Gefüges durch Kopplung von Temperatur- und Gefügemodellierung bei der Erstarrung
    • 2.1.2.4. Martensitbildung und Kernaufreißungen im Kern eines Gussproduktes: Vorhersage der Martensitstarttemperatur
  • 2.1.3. Adjustage von Halbzeug, tertiäre Kühlung
    • 2.1.3.1. Vermeidung von Rissen durch Ausscheidungen auf Korngrenzen
    • 2.1.3.2. Vermeidung von Rissen durch Ferritfilmen auf Korngrenzen
    • 2.1.3.3. Vermeidung von Rissen durch Martensitbildung auf der Oberfläche
  • 2.1.4. Wiedererwärmung vor der Warmumformung oder der Wärmebehandlung
    • 2.1.4.1. Austenitisierung
    • 2.1.4.2. Auflösung von Ausscheidungen, damit diese in nachfolgendem Prozess gezielt und homogen ausgeschieden werden können (z.B. thermomechanisches Walzen mikrolegierter Stähle).
    • 2.1.4.3. Beeinflussung der Randentkohlung
    • 2.1.4.4. Beeinflussung von Kornwachstum durch Ausscheidungen
  • 2.1.5. Warmwalzen, Schmieden und Strangpressen Vorhersage von Phasenumwandlungen durch Berücksichtigung von Umformenergie im Metallvolumen:
    • 2.1.5.1. Festlegung Endwalztemperatur beim thermomechanischen Walzen von mikrolegiertem Stahl mit nicht-rekristallisiertem Gefüge im letzten Stich: Vorhersage der A3-Temperatur (Bildung von Ferrit aus Austenit im Gleichgewicht) unter Berücksichtigung der Umformenergie (Umformgrades, Versetzungsdichte)
    • 2.1.5.2. Ausscheidung (auch deformationsinduziert) und die Beeinflussung der Fließspannung/Rekristallisation/Kornwachstum
    • 2.1.5.3. Laminarkühlung von Warmband: Kinetik der Gefügebildung bei der Austenitumwandlung (Keimbildung und Wachstum von Ferrit, Zementit, Bainit, Martensit)
  • 2.1.6. Wärmebehandlung Grobblech
    • 2.1.6.1. Einstellung eines homogenen martensitischen Gefüges über die gesamte Dicke durch geeignete Temperaturführung unter Berücksichtigung der Martensitstarttemperatur, die dickenabhängig durch die Ausscheidung von Karbiden beeinflusst wird.
    • 2.1.6.2. Bestimmung der Curie-Temperatur zur Festlegung des Transportes durch Magnetkrane.
    • 2.1.6.3. Steuerung der Abkühlkühlung von Blechen und Brammen zur Maximierung der Wasserstoffeffusionsgeschwindigkeit.
  • 2.1.7. Wärmebehandlung Bandanlagen (kontinuierlich, Flachprodukte inkl. Schmelztauchveredelung): Mehrphasenstähle, Festlegung der Temperatur beim interkritischen Glühen, Berechnung des Austenitanteils vor dem beschleunigten Kühlen von Mehrphasenstählen (Mehrphasenstähle wie Dualphasen-, TRIP-, Quench & Partitioning Stahl)
  • 2.1.8. Wärmebehandlung (Coil in Haubenglühe): kontrollierte Ausscheidung zur Steuerung der Rekristallisation zur Einstellung des „Pancake-Gefüges“ von Tiefziehstählen.
  • 2.1.9. Wärmebehandlung von Langprodukten (Träger, Stabstahl, Draht, nahtlose Rohre), Schmiedeteilen und Ringen Vergüten (Härten und Anlassen): Kinetik der Gefügebildung bei der Austenitumwandlung (Keimbildung und Wachstum von Ferrit, Zementit, Bainit, Martensit), Randentkohlung, Aufkohlung, Weichglühen (GKZ-Glühen), Diffusionsglühen (z.B. Kugellagerstähle), Wasserstoff-Effusionsglühen (Flockenfrei-Glühung), ...
  • 2.2. Erhöhung der Genauigkeit von numerischen Temperaturfeldberechnungen mit und ohne Umformung durch:
    • 2.2.1. Berücksichtigung des Enthalpiebeitrages von komplex zusammengesetzten Phasen (z.B. Sigma-Phase in rostfreien Stählen),
    • 2.2.2. Verbesserte Berechnung der Dichte und des thermischen Ausdehnungskoeffizienten durch Berücksichtigung komplex zusammengesetzter Phasen.

2. Purpose of the invention:

• 2.1. Improving the quality of semi-finished products by improving the microstructure and the mechanical-technological properties to avoid defects in the semi-finished product (e.g. cracking) through thermodynamic-kinetic modeling of the microstructure and the resulting phases as a function of the process parameters and the chemical composition (a melt and its scatter ):
  • 2.1.1. Steelmaking: Calculation of thermophysical properties of the melt
  • 2.1.2. Continuous Casting including Twin-Roll Casting, (Twin-) Belt Casting, Block Casting, Continuous Ingot Casting:
    • 2.1.2.1. Prediction of crack susceptibility and avoidance of cracks
      • - Avoidance of hot cracks: prediction of the zero toughness temperature, the thermal contraction of the solid phases in the crack-sensitive temperature interval
      • - Prevention of precipitation-induced surface cracks:
        • (chemical driving force of the nucleation of precipitations on grain boundaries and in the grain)
      • - Avoidance of ferrite films on grain boundaries: (chemical driving force of nucleation of ferrite on grain boundaries and in the grain)
      • - Minimizing the effect of embrittling intermetallic phases (eg Sigma or Laves phase) in continuously cast products
      • - Grain refinement (near the surface)
      • - Core ruptures or delaminations due to liquid films on grain boundaries.
      • - Avoidance of surface cracks with intensive cooling (surface temperatures are in the range of phase transformation) for "thermal soft reduction"
    • 2.1.2.2. Reduction of macrosegregations and carbide ridges in the core of a cast product
    • 2.1.2.3. Adjustment of a cast structure with a defined proportion of globulitic structure by coupling temperature and structure modeling during solidification
    • 2.1.2.4. Martensite formation and core cracking in the core of a cast product: Martensite start temperature prediction
  • 2.1.3. Finishing of semi-finished products, tertiary cooling
    • 2.1.3.1. Avoidance of cracks caused by precipitations on grain boundaries
    • 2.1.3.2. Prevention of cracks caused by ferrite films on grain boundaries
    • 2.1.3.3. Prevention of cracks caused by martensite formation on the surface
  • 2.1.4. Reheating before hot working or heat treatment
    • 2.1.4.1. austenitization
    • 2.1.4.2. Dissolution of precipitates so that they can be separated out in a targeted and homogeneous manner in the subsequent process (e.g. thermomechanical rolling of micro-alloyed steels).
    • 2.1.4.3. Influencing the edge decarburization
    • 2.1.4.4. Influence of grain growth by precipitation
  • 2.1.5. Hot rolling, forging and extrusion Prediction of phase transformations by considering strain energy in the metal volume:
    • 2.1.5.1. Determination of the final rolling temperature during thermomechanical rolling of micro-alloyed steel with a non-recrystallized structure in the last pass: prediction of the A3 temperature (formation of ferrite from austenite in equilibrium) taking into account the deformation energy (degree of deformation, dislocation density)
    • 2.1.5.2. Precipitation (also deformation-induced) and the influence on yield stress/recrystallization/grain growth
    • 2.1.5.3. Laminar cooling of hot strip: kinetics of structure formation during austenite transformation (nucleation and growth of ferrite, cementite, bainite, martensite)
  • 2.1.6. Heat treatment of heavy plate
    • 2.1.6.1. Adjustment of a homogeneous martensitic structure over the entire thickness by suitable temperature control, taking into account the martensite start temperature, which is influenced by the precipitation of carbides depending on the thickness.
    • 2.1.6.2. Determination of the Curie temperature to determine the transport by magnetic cranes.
    • 2.1.6.3. Controlling the cooldown of sheet and slab to maximize hydrogen effusion rate.
  • 2.1.7. Heat treatment of coil lines (continuous, flat products including hot-dip coating): multi-phase steels, determination of the temperature during intercritical annealing, calculation of the proportion of austenite before accelerated cooling of multi-phase steels (multi-phase steels such as dual-phase, TRIP, quench & partitioning steel)
  • 2.1.8. Heat treatment (coil in batch annealing): controlled precipitation to control recrystallization to set the "pancake structure" of deep-drawing steels.
  • 2.1.9. Heat treatment of long products (beams, bar steel, wire, seamless tubes), forgings and rings Tempering and tempering (hardening and tempering): kinetics of structure formation during austenite transformation (nucleation and growth of ferrite, cementite, bainite, martensite), surface decarburization, carburization, soft annealing ( GKZ annealing), diffusion annealing (e.g. ball bearing steels), hydrogen effusion annealing (flake-free annealing), ...
  • 2.2. Increasing the accuracy of numerical temperature field calculations with and without transformation through:
    • 2.2.1. Consideration of the enthalpy contribution of phases with a complex composition (e.g. sigma phase in stainless steels),
    • 2.2.2. Improved calculation of density and thermal expansion coefficient by considering complex composed phases.

• 1 eine beispielshafte Aufstellung von Modellen zur Verwendung im Rahmen des erfindungsgemäßen Verfahrens zur Planung und/oder Steuerung und/oder Regelung eines Herstellungsprozesses in einer metallurgischen Produktionsanlage mit mehreren aufeinanderfolgenden Prozessschritten,
• 2 eine beispielhafte Kopplung von Modellen im Rahmen des erfindungsgemäßen Verfahrens zur Berechnung einer Fließspannung und Walzkraft unter Berücksichtigung der Austenitumwandlung,
• 3 eine beispielhafte Nutzung von Modellen im Rahmen des erfindungsgemäßen Verfahrens entlang der Prozesskette eines Herstellungsprozesses in einer metallurgischen Produktionsanlage,
• 4 eine schematische Darstellung von Temperaturbereichen, in denen Werkstoff-SubModelle zur Anwendung kommen,

The invention is explained in more detail below with reference to some exemplary embodiments illustrated in the accompanying figures. Show it:

• 1 an exemplary list of models for use within the scope of the method according to the invention for planning and/or controlling and/or regulating a manufacturing process in a metallurgical production plant with several successive process steps,
• 2 an exemplary coupling of models within the scope of the method according to the invention for calculating a yield stress and rolling force, taking into account the austenite transformation,
• 3 an exemplary use of models within the scope of the method according to the invention along the process chain of a manufacturing process in a metallurgical production plant,
• 4 a schematic representation of temperature ranges in which material sub-models are used,

1 shows an exemplary list of models for use within the scope of the method according to the invention for planning and/or controlling and/or regulating a production process in a metallurgical production plant with a number of successive process steps.

The basis for the method according to the invention is the basic model 1 for mapping the material behavior in metal production and processing, in particular of metals or metal alloys. The basic model 1 uses the CALPHAD method (Calculation of Phase Diagrams) to describe the properties of these materials, whereby the CALPHAD method models phases with several sublattices and thus thermodynamic properties, thermophysical properties and/or properties derived from them of metallic alloys describes the manufacturing process in the metallurgical plant. In particular, the base model 1 includes all of the manufacturing process, in particular the process steps of the manufacturing process, necessary phases and their properties, which are relevant through, in particular diffusion-controlled, phase transformations or diffusion processes in casting, hot forming, cold forming and / or heat treatment processes.

The basic model 1 preferably relates to the thermodynamic description of phases with several sublattices in metallic alloys, slags, gases and/or ion crystals with electrically charged components or ionic melts. Expediently, the basic model 1 comprises two sub-lattices, one sub-lattice containing the interstitial components (“constituents”, e.g. chemical elements) with lattice gaps (“vacancies”) and the other sub-lattice containing the substitutionally dissolved components.

For example, the basic model 1 describes one or more of the following thermodynamic properties, thermophysical properties and/or properties derived therefrom in thermodynamic equilibrium, in metastable or partial equilibria: chemical potentials, Gibbs energies of phases, phase compositions, phase fractions, enthalpy, heat capacity, density, thermal expansion coefficients, molar volume, viscosity, thermal conductivity, electrical conductivity, magnetic properties, temperatures of phase transformations such as liquidus temperature or solidus temperature, atomic mobilities and diffusion coefficients and/or interfacial energies between phases.

One or more sub-models 2 access the base model 1 and receive and further process the data of the base model 1 created, with the sub-models 2 describing microstructures or material properties from the manufacturing process in the metallurgical plant. The submodels 2 at least partially simulate the kinetics of phase transformations and/or calculate thermodynamic properties that are determined by phase fractions and the chemical composition of phases.

The at least one submodel 2 describes one of the following microstructures or one of the following properties deviating from thermodynamic equilibrium: thermophysical properties, precipitation/dissolution, in particular of intermetallic phases, microsegregation/solidification kinetics, austenitization, formation of defects (hot cracks, blowholes, pores, vacancies, dislocations) , austenite decay, dendrite growth, martensitic transformations, cast structure (columnar-to-equiaxed transition), strengthening/softening (recovery, recrystallization), equalization of differences in concentrations and chemical potentials (diffusion, hydrogen effusion), flow stress, grain growth, formation of crystallographic textures ( e.g. Goss texture in grain-oriented silicon steel), mechanical properties and/or magnetic properties such as the Curie temperature. In particular, several sub-models 2 are created, each of which receives and further processes the information from the created base model 1, with each sub-model describing 2 microstructure or material properties from the manufacturing process in the metallurgical plant.

Expediently, data can be exchanged between the base model 1 and the sub-models 2, or among the sub-models 2 among themselves.

The method according to the invention is also based on process models 3 for the several successive process steps of the manufacturing process in the metallurgical production plant, the process models 3 created optimizing the manufacturing process of the respective process step on the basis of one or more of the sub-models 2 created. In order to optimize an associated process step of the manufacturing process, the associated process model 3 determines, for example, controlled variables and/or manipulated variables for automating the manufacturing process.

The controlled variables are selected in particular from: viscosity of the melt, phase fraction, in particular solid phase fraction and melt fraction, data on precipitations (in particular their type, volume fraction, mean size and/or distribution function of the size of the precipitations, Zener pinning force), mean grain sizes and/or or distribution function of the grain sizes, microstructure proportions (e.g. bainite, pearlite), dislocation density, mechanical properties, formability, formability, Cottrell clouds, degree of homogenization, overheating of the melt at the start of casting to the end of casting, position of solidification, strand shell thickness, strand surface temperatures, position of the specified phase parts, drawing temperature , geometry and dimensions of the semi-finished product/end product (e.g. additively manufactured, forged, ...), final rolling temperature, structure before forming, drawing temperature from the reheating process, mass flow, rolling speed and/or coiling temperature.

The manipulated variables preferably relate to process parameters such as temperature, time, speed, pressure, force, volume flows and the like and are selected in particular from: tapping temperature of the melt, heating power, cooling scrap, flushing time, casting speed, water quantities and air pressure of the secondary cooling, temperature and residence time in heating zones, operating temperature and/or degree of deformation. Data can expediently be exchanged between the process models 3, the sub-models 2 and/or the base model 1. Thus, for example, in addition to the chemical composition and the temperature, calculation results of the sub-models 2 and the process models 3 can also be transferred to the basic model 1, so that the results of the basic model 1 are specified and transferred back to the sub-models 2 in order to carry out an iterative calculation.

The method according to the invention optimizes the production process in the metallurgical plant with the multiple successive process steps, taking into account the process models 3 created and global and/or local optimization goals. The global optimization goals relate, for example, to the entire manufacturing process in the metallurgical production plant, and the local optimization goals relate, for example, to one or more of the process steps of the manufacturing process in the metallurgical production plant.

The optimization goals are expediently selected from a group of characteristics made up of: product quality, product defects, product chemical composition, energy consumption, production rate, machine utilization, machine wear and/or production time.

In particular, the process model 3 provides information about the temperature control, the mass flow and/or the forming in order to optimize the manufacturing process.

2 shows an exemplary coupling of models within the scope of the method according to the invention for calculating a yield stress and rolling force, taking into account hardening and softening and phase transformations (austenite transformation, precipitation formation). The models influence each other: The base model 1 provides information on the material sub-models a)-e), from which the base model in turn receives information on the dislocation density/energy, whereby the base model provides improved information on the phase transformations (iterative calculation) . With this iteratively calculated data of the phase transformations, the yield stress is calculated, which is used by the process model 3 to calculate the rolling force. The process model calculates, among other things, the temperature and the deformation.

3 shows an exemplary use of models within the scope of the method according to the invention along the process chain of a manufacturing process in a metallurgical production plant. In particular shows 3 the use of different sub-models 2 through the successive process steps of melting, continuous casting, intermediate cooling, reheating, rolling and cooling in the process chain for hot strip according to the CSP method (Compact Strip Production).

4 shows a schematic representation of temperature ranges in the hot strip process chain for steel from the melt to coil cooling, in which material sub-models 2 are used (RT: room temperature, T _A1 : equilibrium austenite formation temperature during heating, T _A3 : equilibrium temperature of ferrite formation during cooling , Ts: solidus temperature of solidification, T _L : liquidus temperature of solidification).

Reference List

1

base model

2

submodels

3

process models

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• EP 1289691 B2 [0003, 0006]
• DE 1025171683 A1 [0008]

Non-patent Literature Cited

• S. Hahn, T. Schaden: "Dynaphase: Online Calculation of Thermodynamic Properties during Continuous Casting", Berg- und Hüttenmännische Zeitenhefte, BHM (2014) Vol. 159 (11), pp. 438-446 [0003]

Claims (18)

Method for planning and/or controlling and/or regulating a manufacturing process in a metallurgical production plant with a plurality of successive process steps, comprising the steps: Creation of a base model (1) to depict the material behavior in metal production and processing, in particular of metals or metallic alloys, the base model (1) for property descriptions of these materials using the CALPHAD method (Calculation of Phase Diagrams), where the CALPHAD method models phases with multiple sub-lattices and thereby describes thermodynamic properties, thermophysical properties and/or properties derived therefrom of metallic alloys from the manufacturing process in the metallurgical plant, Creation of at least one sub-model (2), which receives and further processes the information of the created basic model (1), the sub-model (2) describing structure or material properties from the manufacturing process in the metallurgical plant, Creation of process models (3) for the several successive process steps of the manufacturing process in the metallurgical production plant, the created process models (3) optimizing the manufacturing process of the respective process step on the basis of one or more of the created sub-models (2), and Optimizing the manufacturing process in the metallurgical plant with the multiple successive process steps, taking into account the process models (3) created and global and/or local optimization goals. procedure after claim 1 , where the basic model (1) describes the thermodynamic description of phases with several sublattices in metallic alloys, slags, gases and/or ionic crystals with electrically charged components or ionic melts. procedure after claim 1 or 2 , whereby the basic model (1) describes one or more of the following thermodynamic properties, thermophysical properties and/or properties derived therefrom in thermodynamic equilibrium, in metastable or partial equilibria: chemical potentials, Gibbs energies of phases, phase compositions, phase fractions, enthalpy, heat capacity , density, thermal expansion coefficients, molar volume, viscosity, thermal conductivity, electrical conductivity, magnetic properties, temperatures of phase transitions such as liquidus temperature or solidus temperature, atomic mobilities and diffusion coefficients and/or interfacial energies between phases. Procedure according to one of Claims 1 until 3 , wherein the basic model (1) includes all phases necessary for the manufacturing process, in particular the process steps of the manufacturing process, and their properties that are relevant due to, in particular, diffusion-controlled phase transformations or diffusion processes in casting, hot forming, cold forming and/or heat treatment processes . Procedure according to one of Claims 1 until 4 , wherein at least one sub-model (2) simulates the kinetics of phase transformations and/or calculates thermodynamic properties that are determined by phase fractions and the chemical composition of phases. Procedure according to one of Claims 1 until 5 , wherein the at least one submodel (2) describes one of the following microstructures or one of the following properties deviating from thermodynamic equilibrium: thermophysical properties, precipitation/dissolution, in particular of intermetallic phases, microsegregation/solidification kinetics, austenitization, formation of defects (hot cracks, blowholes, pores , vacancies, dislocations), austenite decay, dendrite growth, martensitic transformations, cast structure (columnar-to-equiaxed transition), strengthening and softening (recovery, recrystallization), equalization of differences in concentrations and chemical potentials (diffusion, hydrogen effusion), yield stress, grain growth , Development of crystallographic textures (e.g. Goss texture in grain-oriented silicon steel), mechanical properties and/or magnetic properties such as the Curie temperature. Procedure according to one of Claims 1 until 6 , Data being exchanged between the base model (1), the plurality of sub-models (2) and/or the plurality of process models (3). Procedure according to one of Claims 1 until 7 , wherein one or more process models (3) to optimize an associated process step of the manufacturing process determine control variables and / or manipulated variables for the automation of the manufacturing process. procedure after claim 8 , where the controlled variables are selected from: viscosity of the melt, phase fraction, in particular solid phase fraction and melt fraction, data on precipitations (in particular their type, volume fraction, mean size and/or distribution function of the size of the precipitations, Zener pinning force), mean grain sizes and /or distribution function of the grain sizes, microstructure proportions (e.g. bainite, pearlite), dislocation density, mechanical properties, formability, formability, Cottrell clouds, degree of homogenization, overheating of the melt at the start of casting to the end of casting, position of solidification, strand shell thickness, strand surface temperatures, position of the specified phase parts, Drawing temperature, geometry and dimensions of the semi-finished product/end product (e.g. additively manufactured, forged, ...), final rolling temperature, structure before forming, drawing temperature from the reheating process, mass flow, rolling speed and/or coiling temperature. procedure after claim 8 or 9 , whereby the manipulated variables relate to process parameters such as temperature, time, speed, pressure, force, volume flows and the like and are selected in particular from: tapping temperature of the melt, heating power, cooling scrap, flushing time, casting speed, water quantities and air pressure of the secondary cooling, temperature and residence time in heating zones, Operating temperature and/or degree of deformation. Procedure according to one of Claims 1 until 10 , wherein the multiple sequential process steps of the manufacturing process are selected from a group of features formed from: melting and alloying, continuous casting, ingot casting, finishing, reheating, hot rolling, cold rolling, heat treatment, surface coating, surface hardening, forging, extrusion, additive manufacturing and / or transportation . Procedure according to one of Claims 1 until 11 , where the global optimization goals concern the entire manufacturing process in the metallurgical production plant. Procedure according to one of Claims 1 until 12 , where the local optimization goals relate to one or more of the process steps of the manufacturing process in the metallurgical production plant. procedure after claim 12 or 13 , wherein the optimization goals are selected from a group of characteristics formed from: product quality, product defects, product chemical composition, energy consumption, production rate, machine utilization, machine wear and/or production time. Procedure according to one of Claims 1 until 14 , wherein several sub-models (2) are created, which each receive and further process the information of the created base model (1), each sub-model (2) describing structure or material properties from the manufacturing process in the metallurgical plant. Procedure according to one of Claims 1 until 15 , The process model (3) providing information on temperature control, mass flow and/or forming in order to optimize the manufacturing process. Procedure according to one of Claims 1 until 16 , whereby the base model (1) is also given the calculation results of the sub-models (2) and the process models (3) in addition to the chemical composition and the temperature, so that the results of the base model (1) are specified and passed back to the sub-models (2). to perform an iterative calculation. Procedure according to one of Claims 1 until 17 , wherein the base model (1) comprises two or more sublattices, one sublattice containing the interstitial components with vacancies and the other sublattice containing the substitutionally solved components.

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